David Marlevi (credit: Ulf Sirborn)

David Marlevi


I am a research lead in quantitative cardiovascular imaging, developing data-driven image analysis tools to tackle urgent clinical challenges across the heart, aorta, and brain.

Om mig

I am a biomedical engineer with a focus on translational cardiovascular imaging. Specifically, I am intrigued by the translation of image-based engineering into clinical practice, using data-driven utilities to enhance diagnosis, improve prognosis, and provide fundamental mecanistic understanding of cardiovascular disease. 

After graduating from the joint doctoral program in Medical Technology from the Royal Institute of Technology (KTH) and KI with a thesis entitled "Non-invasive imaging for improved cardiovascular care", I spent two years as a postdoctoral fellow at the Massachusetts Institute of Technology (MIT), funded by a Knut and Alice Wallenberg foundation scholarship and working under the tutelage of Prof. Elazer R. Edelman (edelmanlab). At MIT, I worked on AI-driven image analysis to monitor intravascular interventions, as well as lead lab efforts on novel vascular drug delivery systems. In 2021, I returned to Sweden and KI as a research lead in quantitative cardiovascular imaging, working closely with clinical and technical fellows at both KI and the Karolinska University Hospital to translate advanced image technologies into clinical practice. Specific focuses has been on hemodynamic mapping by full-field phase-contrast magnetic resonance imaging (4D Flow MRI), with coupled physics-informed analysis allowing for regional hemodynamic quantifications across the cardiovascular system. 


Non-invasive estimation of cardiovascular pressure gradients: Regional quantification of cardiovascular pressure gradients is critical for diagnosis, treatment planning, and risk prediction of many cardiovascular disease. Still, for a large number of conditions, non-invasive assessment is obstructed by inherent method limitations, and a wide range of clinical instances exist where regional pressure behaviour remains unexplored. To tackle this, we have recently deployed a combination of physics-informed image analysis (invoking fundamental fluid mechanical description of blood flow) and full-field flow imaging (4D Flow MRI) to allow for arbitrary probing of pressure gradients across previously inaccessible compartments. Here, we seek to extend these utilities to further understand early hemodynamic changes indicative of later physiological impairement, including validation, implementation, and clincial utility across spatial (large / small vessels), temporal (fast / slow flows) and flow (laminar / turbulent) scales. 

Super-resolution 4D Flow MRI: The advent of full-field flow imaging by 4D Flow MRI has fundamentally changed our ability to interrogate complex hemodynamic behaviour in a direct clinical setting. However, spatiotemporal limitations exist based on the clinical time frames in which the systems can be used, obstructing assessment of regional or highly transient flow events. To tackle this, we have recently employed deep residual networks to enhance spatial image resolution, effectively pushing quantitative 4D imaging into challenging intracranial vessels. Now, we seek to extend the same utilities into temporally challenging flows such as in the heart, or through complex aortic disease. Further inclusion of so called physics-informed networks are also expected to expand clinical impact and versatility of 4D Flow MRI across a wide cardiovascular application range.

Akademiska priser och utmärkelser

  • Early Career Award – Translational Science, Society for Cardiovascular Magnetic Resonance (SCMR), 2021
  • Runner-up, Best presentation in Basic science, 1st Annual Marvin M. Kirsh Resident Research Symposium, University of Michigan, 2021
  • Potchen-Pasariello Award – Best presentation in Clinical Science, Society for Magnetic Resonance Angiography (SMRA), 2020
  • Trainee grant, IEEE Nuclear Science Symposium and Medical Imaging Conference, 2015
  • Travel award, IEEE International Ultrasonics Symposium, 2015
  • KTH Best graduate student of the year, KTH Royal Institute of Technology, 2014
  • Endeavour Research Award, Australian Government Research Award fellowship
  • Henrik Göransson Sandviken scholarship, 2011
  • Hjalmar Berwalds minne för framstående matematiska studier, 2010